An Efficient Implementation of the Synchronization Likelihood Algorithm for Functional Connectivity

Abstract

Measures of functional connectivity are commonly employed in neuroimaging research. Among the most popular measures is the Synchronization Likelihood which provides a non-linear estimate of the statistical dependencies between the activity time courses of different brain areas. One aspect which has limited a wider use of this algorithm is the fact that it is very computationally and memory demanding. In the present work we propose new implementations and parallelizations of the Synchronization Likelihood algorithm with significantly better performance both in time and in memory use. As a result both the amount of required computational time is reduced by 3 orders of magnitude and the amount of memory needed for calculations is reduced by 2 orders of magnitude. This allows performing analyses that were not feasible before from a computational standpoint.

Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity

“…Development of GPU processors driven by the game consumer industry has produced outstanding achievements and a change in the way scientists see GPU computation (Nickolls and Dally, 2010 ). Creating an incessant stream of successfully translated algorithms and applications into GPU makes us believe it is worth studying the advantages and disadvantages of porting FC and network indices to GPU oriented implementations (see Rosales et al, 2015 ; Wollstadt et al, 2014 for a close related example). Thus, as a community we shall make sure this field takes full advantage of heterogeneous multicore CPU/GPU architectures.…”

Assessing cortical synchronization during transcranial direct current stimulation: A graph-theoretical analysis

“…Specifically, SL was computed for each frequency band using the first 10 artefact-free epochs for each subject and between all pairs of channels during each tDCS session. SL was chosen due to its capacity to detect both linear and non-linear coupling between signals (Stam and van Dijk, 2002;Montez et al, 2006;Rosales et al, 2014). The methods for the calculation of SL as well as the choice of its parameters are reported in the supplementary material.…”

High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

“…Bastos and Schoffelen [47] provided a review of some methods, such as coherence, phase synchronization, phase-slope index. Rosales et al [48] proposed a new implementation of the Synchronization Likelihood algorithm, which improves significantly its computational and memory performance. Lombardi et al [49] described a new synchronization-based metric which is more sensitive to nonlinear coupling phenomena between time series and more robust with respect to noise.…”